Face recognition performance achieves high accuracy in close proximity. However, great challenges still exist in recognizing human face at long distance. In fact, the rapidly increasing need for long range surveillance requires a passage from close-up distances to long distances which affects strongly the human face image quality and causes degradation in recognition accuracy. To address this problem, we propose in this paper, a multispectral pixel level fusion approach to improve the performance of automatic face recognition at long distance. The main objective of the proposed approach is to formulate a method to enhance the face image quality as well as the face recognition rate. First, visible and near-infrared images are decomposed into a different bands using discrete wavelet transform. Then, the fusion process is performed through the singular value decomposition and principal component analysis. The results highlight further the still challenging problem of face recognition at long distance, as well as the effectiveness of our proposed approach as an alternative solution to this problem.